McGill.CA / Department of Psychology / Research / Research Areas / Quantitative & Modelling

Graduate Studies in Quantitative Psychology & Modelling

Researchers at McGill University aspire to be world-leaders in the development, evaluation, and application of models and methods for the analysis of psychological data. Training in Quantitative Psychology and Modelling is research intensive, with students working closely with one or more faculty members. Students have opportunities to share their research with colleagues in the area, present at a number of professional conferences, and gain hands-on teaching experience. The ultimate goal is to train students to be independent and outstanding quantitative data analysts, who are highly sought after by both academic and industrial institutions.

To read more about Quantitative Psychology and Modelling and a career in this field, see the links on this page.

Applying to the PhD Program

Prospective students interested in studying Quantitative Psychology and Modelling generally apply to the PhD program (https://www.mcgill.ca/psychology/graduate) and most students follow the “Experimental Psychology” track. As the PhD program is mentorship-based, it is recommended to learn more about each faculty member’s research interests to select an appropriate supervisor in your application. However, faculty members in the area often collaborate on research projects and sometimes co-supervise students. Prospective students are encouraged to list all quantitative faculty as possible supervisors on the application summary sheet in order of preference, with the personal statement more clearly outlining specific research interests and a proposed supervisor.

Preparation

Preparation for graduate studies in Quantitative Psychology is often supervisor and research topic dependent. Coursework from advanced undergraduate courses in applied statistics in psychology, or undergraduate courses or research experiences acquired in statistics or computer science present transferrable background skills but are not necessary prior to starting graduate studies.

Curriculum

PhD students following the Experimental Psychology track generally must fulfill or be exempted from (based on prior coursework) two introductory statistics course requirements on basic statistics such as ANOVA and multiple linear regression as well as a sampling of multivariate techniques (PSYC 650/651). Aside from these and two professional development courses (PSYC 660D1/660D2), students take a minimum of 4 area seminars, 2 of which must be within the department. Area seminars in quantitative psychology generally cover modelling techniques such as Multilevel Modeling, Structural Equation Modeling, Applied Bayesian Statistics, Measurement, Item Response Theory, and Machine Learning. Students in the PhD program are encouraged to take complementary courses from other departments (Biostatistics, Math/Stats, and Computer Science) such as Causal Inference, Generalized Linear Models, Machine Learning, Nonparametric Statistics, Mathematical Statistics, Statistical Inference, Optimization, Natural Language Processing, and so on. Quebec universities also enjoy a collaborative policy: Should another course also be available at another public university in Montreal (e.g., Concordia, Universite de Montreal, Universite du Quebec), attendance of that course is also a possibility (https://www.mcgill.ca/transfercredit/iut).

To find out more about the formal coursework requirements, see here for information about the PhD program and refer to the Clinical or Experimental links (https://www.mcgill.ca/psychology/graduate/program-tracks), refer to the Graduate Student Handbook on this page (https://www.mcgill.ca/psychology/graduate/current-students), or the official PhD program requirements at the registrar (https://www.mcgill.ca/study/).

Faculty Members

Core Faculty

Carl F. Falk - Faculty Profile, Personal Website
Jessica Kay Flake - Faculty Profile, Personal Website
Heungsun Hwang - Faculty Profile, Personal Website
Milica Miočević - Faculty Profile, Personal Website

Faculty Lecturer

Jens Kreitewolf - Faculty Profile, Laboratory

Affiliated Faculty

Brendan Johns - Faculty Profile, Personal Website
Tom Shultz - Faculty Profile, Personal Website

Resources

The research environment and facilities at McGill provide ample opportunities for collaboration, professional development, and computationally intensive research.

The Computational and Data Systems Initiative (https://www.mcgill.ca/cdsi/) offers additional research collaboration, training, and teaching/consulting opportunities. Additional training and talks are sometimes available through Mila (https://mila.quebec/).

McGill quantitative methods faculty and students also enjoy accesses to high performance computing clusters hosted by Calcul Québec (https://www.calculquebec.ca/) / Compute Canada (https://www.computecanada.ca/).

Recent Graduates

Gyeongcheol Cho (PhD, 2023) 

  • Assistant Professor at the Ohio State University

Sunmee Kim (PhD, 2020)

  • Assistant professor at University of Manitoba, Canada

Ji Yeh Choi (PhD, 2017)

  • Assistant professor at York University, Canada

Ahmed Khalil Ben Ayed (Post-doctoral, 2017)

  • Assistant Professor at University of Ottawa, Canada

Jyungkyu Park (PhD, 2016)

  • Assistant Professor at Kyungpook National University, South Korea

Tian-Yu Tan (PhD, 2014)

  • Vice President of Quantitative Equity Portfolio Management and Research, TD Asset Management

Lixing Zhou (PhD, 2014)

  • Commercial Underwriter, OnDeck Canada

Héla Romdhani (Post-Doctoral, 2014)

  • Associate Consultant, Analysis Group, Canada

Hye Won Suk (PhD, 2013)

  • Associate Professor at Sogang University, South Korea

Kwanghee Jung (PhD, 2011)

  • Assistant Professor at Texas Tech University, USA

Related Professional Societies/Meetings

 

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